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GOLEM  GOLEM - Generalized OntoLogical Environments for Multi-agent systems  An agent environment that can be used to create multi-agent system applications  Agents in several container environment communicate and take decisions

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MARGO & CASAPI  MARGO - Multiattribute ARGumentation framework for Opinion explanation  It is written in Prolog  Implements the ArguGRID argumentation framework about service selection and composition  MARGO is built on top of CASAPI  CASAPI - Credulous and Sceptical Argumentation : Prolog Implementation  It is a general-purpose tool for assumption-based argumentation

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Peer to Peer technology in ARGUGRID  PLATON++ - P2P Load Adjusting Tree Overlay Networks  A new load-balancing framework, to support a distributed K-Dimensional tree system used for multi-attribute queries

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GRID Platform  GRIA is the GRID middleware that ArguGRID uses to support the service – oriented infrastructure  Supports Business to Business collaborations  Provides an SLA module for ArguGRID needs

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Preconditions  Different GRIA host machines that store the offered services along with their SLAs. Each service has to be wrapped as a GRIA service  Different machines containing GOLEM containers. Each GOLEM agent is equipped with the CASAPI argumentation engine and is assumed to have basic knowledge as defined by each use case scenario  A peer-to-peer platform, PLATON, runs as underlying middleware with each GOLEM container constituting a PLATON node  Set up of distributed Semantic Registries holding semantic information about the services, upon which the GOLEM agents query  KDE authoring tool interface, where the users enter to set their goals forming abstract workflows

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Fire Monitoring Scenario Description 5. GOLEM agents use PLATON++ P2P platform to discover GRIA GRID services to perform the user request 6. The agents negotiate upon the service constraints in order to satisfy user goals  SLA negotiation about the delivery time, the image quality and the price 7. A concrete workflow is now formed and returned to KDE

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Fire Monitoring Scenario Description 8. The concrete workflow is executed  First a satellite image from the desired area is returned (the appropriate instruments are called)  The image is given as input to the clipping service → a transformed image is returned  The new image is given as input to the fire detection service, which uses the radar/optical instruments to detect the fire  An image with the fire sources marked on it, is returned back to the user

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Fire Detection Scenario Image

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Conclusions  Growing need for Earth Observation products  Easier and timely access to large quantities of primary data is a condition for delivering effective services  Users do not need knowledge about services and instruments utilized  ARGUGRID provides an automatic way to derive information from the Earth Observation Instruments